Shortcut learning refers to the phenomenon where models employ simple, non-robust decision rules in practical tasks, which hinders their generalization and robustness. With the rapid development of large language models (LLMs) in recent years, an increasing number of studies have shown the impact of shortcut learning on LLMs. This paper provides a novel perspective to review relevant research on shortcut learning in In-Context Learning (ICL). It conducts a detailed exploration of the types of shortcuts in ICL tasks, their causes, available benchmarks, and strategies for mitigating shortcuts. Based on corresponding observations, it summarizes the unresolved issues in existing research and attempts to outline the future research landscape of shortcut learning.

翻译：捷径学习指模型在实际任务中采用简单、非鲁棒的决策规则的现象，这会阻碍其泛化能力和鲁棒性。近年来，随着大语言模型（LLMs）的快速发展，越来越多的研究表明了捷径学习对LLMs的影响。本文提供了一个新颖的视角来回顾上下文学习（ICL）中捷径学习的相关研究。它详细探讨了ICL任务中捷径的类型、成因、可用基准以及缓解捷径的策略。基于相应的观察，本文总结了现有研究中未解决的问题，并尝试勾勒出捷径学习的未来研究图景。